Assignment 9, due Sunday Mar 13
SYLLABUS & LECTURE SLIDES:
Math Notation
Nature paper on Avida
Avida web site
Nature paper on human genome sequence
Nature paper on mouse genome sequence
Siepel et al. paper on PhyloHMMs & sequence conservation
Rabiner tutorial on HMMs
HMM scaling tutorial (Tobias Mann)
Supervised learning tutorial
- Biological Review : Gene and genome structure in prokaryotes and eukaryotes; the genetic code & codon usage; "global" genome organization. Sources and characteristics of sequence data; Genbank and other sequence databases.
- Lecture 1: Finding exact matches in sequences using suffix arrays.
- Lecture 2: Algorithmic complexity. Directed graphs; depth structure of directed acyclic graphs (DAGs); trees and linked lists. Reading: Durbin et al. section 2.1, 2.2, 2.3.
- Discussion Section 1: HW1 and general programming tips.
- Lecture 3: Dynamic programming on weighted DAGs. Reading: Durbin et al. 2.4, 2.5, 2.6.
- Lecture 4: Maximal-scoring sequence segments. Edit graphs & sequence alignment. Reading: Durbin et al. 6.1, 6.2, 6.3; Ewens & Grant 1.1, 1.2, 1.12, 3.1, 3.2, 3.4, 3.6, 5.2, 9.1, 9.2
- Discussion Section 2: HW1 & 2, DAGs, more graph algorithms, dynamic programming, RNA folding.
- Lecture 5: Smith-Waterman algorithm. Needleman-Wunsch algorithm. Local vs. global. Multiple sequence alignment. Linear space algorithms. Reading: Ewens & Grant 5.3.1, 5.3.2, 12.1, 12.2, 12.3; Durbin et al. chapter 3
- Lecture 6: Linear space algorithms (cont'd). General & affine gap penalties. Profiles.
- Discussion Section 3: BLAST.
- Lecture 7: Smith-Waterman special cases. Word nucleation approaches/BLAST. Probability models on sequences; review of basic probability theory: probability spaces, conditional probabilities, independence. Reading: Ewens & Grant 12.2, 12.3, 1.14, Appendix B.10; Durbin et al. chapter 3
- Lecture 8: Probabilities on sequences. Failure of equal frequency assumption for DNA. Site models. Site model examples: 3' splice sites, 5' splice sites, protein motifs. Site probability models.
- Discussion Section 4: BOWTIE, MUSCLE.
- Lecture 9: Comparing alternative models. Neyman-Pearson lemma. Weight matrices for site models. Weight matrices for splice sites in C. elegans. Score distributions.
- Lecture 10: Limitations of site models (variable spacing, non-independence). Hidden Markov Models: introduction; formal definition. Reading: Siepel et al.
- Discussion Section 5: motif finding.
- Lecture 11: HMM examples: -- splice sites; 2-state models; 7-state prokaryote genome model. Probabilities of sequences. Computing HMM probabilities via associated WDAG.
- Lecture 12: Computing HMM probabilities via associated WDAG. HMM Parameter estimation: Viterbi training; Baum-Welch (EM) algorithm; techniques for finding global maxima in likelihood surface.
- Discussion Section 6: Viterbi algorithm; bootstrapping.
- Lecture 13: Techniques for finding global maxima in likelihood surface. Detection of evolutionarily conserved regions using Phylo-HMMs.
- Lecture 14: Detection of evolutionarily conserved regions using Phylo-HMMs (cont'd).
- Lecture 15: Detection of evolutionarily conserved regions using Phylo-HMMs (cont'd).
- Lecture 16: Detection of evolutionarily conserved regions using Phylo-HMMs (cont'd).
- Discussion Section 8: EM algorithm, MCMC.
- Lecture 17: Maximal scoring segments. D-segments, relationship to 2-state HMMs.
- Lecture 18: Distribution of maximal segment scores in background sequence. Karlin-Altschul theory.
- Discussion Section 9: Faster Python programs: Cython, profiling.
- Lecture 19: Karlin-Altschul theory (cont'd). Information theory.
- Discussion Section 10: Unix tools. Organizing computational biology projects. Version control, GIT.
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